SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2401–2425 of 3073 papers

TitleStatusHype
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Graph-based Semi-Supervised & Active Learning for Edge FlowsCode0
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active LearningCode0
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields—0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational modelsCode0
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—0
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors—0
Bayesian Generative Active Deep Learning—0
Factored Contextual Policy Search with Bayesian Optimization—0
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays—0
Disagreement-based Active Learning in Online Settings—0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning—0
Active Adversarial Domain Adaptation—0
Exploring Representativeness and Informativeness for Active Learning—0
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion—0
Detecting Repeating Objects using Patch Correlation Analysis—0
BAOD: Budget-Aware Object Detection—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Active Learning for Decision-Making from Imbalanced Observational DataCode0
Context-Aware Query Selection for Active Learning in Event Recognition—0
Generalized active learning and design of statistical experiments for manifold-valued data—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Empirical Evaluations of Active Learning Strategies in Legal Document Review—0
Active Learning for Network Intrusion Detection—0
Sequential Adaptive Design for Jump Regression Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified